Systems biology

418 Pages·2017·7.942 MB·English

by  Hohmann, Stefan, Nielsen, Jens

Editedby
JensNielsenand
StefanHohmann
SystemsBiology
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Systems Biology
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V
Contents
ListofContributors XV
AbouttheSeriesEditors XXIII
1 IntegrativeAnalysisofOmicsData 1
TobiasÖsterlund,MarijaCvijovic,andErikKristiansson
Summary 1
1.1 Introduction 1
1.2 OmicsDataandTheirMeasurementPlatforms 4
1.2.1 OmicsDataTypes 4
1.2.2 MeasurementPlatforms 5
1.3 DataProcessing:QualityAssessment,Quantification,Normalization,
andStatisticalAnalysis 6
1.3.1 QualityAssessment 7
1.3.2 Quantification 9
1.3.3 Normalization 10
1.3.4 StatisticalAnalysis 11
1.4 DataIntegration:FromaListofGenestoBiologicalMeaning 12
1.4.1 DataResourcesforConstructingGeneSets 13
1.4.1.1 GeneOntologyTerms 13
1.4.1.2 KEGGandReactome 13
1.4.1.3 Genome-ScaleMetabolicReconstructions 14
1.4.2 GeneSetAnalysis 14
1.4.2.1 GeneSetOverenrichmentTests 16
1.4.2.2 Rank-BasedEnrichmentTests 16
1.4.3 NetworksandNetworkTopology 17
1.5 OutlookandPerspectives 18
References 19
2 13CFluxAnalysisinBiotechnologyandMedicine 25
YiErnCheah,ClintonM.Hasenour,andJameyD.Young
2.1 Introduction 25
2.1.1 WhyStudyMetabolicFluxes? 25
2.1.2 WhyareIsotopeTracersImportantforFluxAnalysis? 26
VI Contents
2.1.3 HowareFluxesDetermined? 28
2.2 TheoreticalFoundationsof13CMFA 29
2.2.1 ElementaryMetaboliteUnits(EMUs) 30
2.2.2 FluxUncertaintyAnalysis 31
2.2.3 OptimalDesignofIsotopeLabelingExperiments 32
2.2.4 IsotopicallyNonstationaryMFA(INST-MFA) 34
2.3 MetabolicFluxAnalysisinBiotechnology 36
2.3.1 13CMFAforHostCharacterization 36
2.3.2 13CMFAforPinpointingYieldLossesandFutileCycles 39
2.3.3 13CMFAforBottleneckIdentification 41
2.4 MetabolicFluxAnalysisinMedicine 42
2.4.1 LiverGlucoseandOxidativeMetabolism 43
2.4.2 CancerCellMetabolism 47
2.4.3 FuelOxidationandAnaplerosisintheHeart 48
2.4.4 MetabolisminOtherTissues:Pancreas,Brain,Muscle,Adipose,and
ImmuneCells 49
2.5 EmergingChallengesfor13CMFA 50
2.5.1 TheoreticalandComputationalAdvances:MultipleTracers,
Co-cultureMFA,DynamicMFA 50
2.5.2 Genome-Scale13CMFA 51
2.5.3 NewMeasurementStrategies 52
2.5.4 High-ThroughputMFA 53
2.5.5 ApplicationofMFAtoIndustrialBioprocesses 53
2.5.6 IntegratingMFAwithOmicsMeasurements 54
2.6 Conclusion 55
Acknowledgments 55
Disclosure 55
References 55
3 MetabolicModelingforDesignofCellFactories 71
MingyuanTian,PrashantKumar,SanjanT.P.Gupta,andJenniferL.Reed
Summary 71
3.1 Introduction 71
3.2 BuildingandRefiningGenome-ScaleMetabolicModels 72
3.2.1 GenerateaDraftMetabolicNetwork(Step1) 74
3.2.2 ManuallyCuratetheDraftMetabolicNetwork(Step2) 75
3.2.3 DevelopaConstraint-BasedModel(Step3) 77
3.2.4 RevisetheMetabolicModelthroughReconciliationwith
ExperimentalData(Step4) 79
3.2.5 PredictingtheEffectsofGeneticManipulations 81
3.3 StrainDesignAlgorithms 83
3.3.1 FundamentalsofBilevelOptimization 84
3.3.2 AlgorithmsInvolvingOnlyGene/ReactionDeletions 94
3.3.3 AlgorithmsInvolvingGeneAdditions 94
3.3.4 AlgorithmsInvolvingGeneOver/Underexpression 95
Contents VII
3.3.5 AlgorithmsInvolvingCofactorChanges 98
3.3.6 AlgorithmsInvolvingMultipleDesignCriteria 99
3.4 CaseStudies 100
3.4.1 StrainsProducingLactate 100
3.4.2 StrainsCo-utilizingSugars 100
3.4.3 StrainsProducing1,4-Butanediol 102
3.5 Conclusions 103
Acknowledgments 103
References 104
4 Genome-ScaleMetabolicModelingandInsilicoStrainDesignof
Escherichiacoli 109
MeiyappanLakshmanan,Na-RaeLee,andDong-YupLee
4.1 Introduction 109
4.2 TheCOBRAApproach 110
4.3 HistoryofE.coliMetabolicModeling 111
4.3.1 Pre-genomic-eraModels 111
4.3.2 Genome-ScaleModels 112
4.4 InsilicoModel-BasedStrainDesignofE.coliCellFactories 115
4.4.1 GeneDeletions 127
4.4.2 GeneUp/Downregulations 127
4.4.3 GeneInsertions 128
4.4.4 CofactorEngineering 128
4.4.5 OtherApproaches 128
4.5 FutureDirectionsofModel-GuidedStrainDesigninE.coli 129
References 130
5 AcceleratingtheDrugDevelopmentPipelinewithGenome-Scale
MetabolicNetworkReconstructions 139
BonnieV.Dougherty,ThomasJ.MoutinhoJr.,andJasonPapin
Summary 139
5.1 Introduction 139
5.1.1 DrugDevelopmentPipeline 140
5.1.2 OverviewofGenome-ScaleMetabolicNetwork
Reconstructions 140
5.1.3 AnalyticalToolsandMathematicalEvaluation 141
5.1.3.1 FluxBalanceAnalysis(FBA) 141
5.1.3.2 FluxVariabilityAnalysis(FVA) 142
5.2 MetabolicReconstructionsintheDrugDevelopmentPipeline 142
5.2.1 TargetIdentification 143
5.2.2 DrugSideEffects 145
5.3 Species-LevelMicrobialReconstructions 146
5.3.1 MicrobialReconstructionsintheAntibioticDevelopment
Pipeline 146
5.3.1.1 ApplicationsintheDrugDevelopmentPipeline 146
VIII Contents
5.3.2 Metabolic-Reconstruction-FacilitatedRationalDrugTarget
Identification 147
5.3.2.1 TargetingGenesEssentialforBiomassProduction 147
5.3.2.2 TargetingVirulenceFactors 147
5.3.2.3 Metabolite-centricTargeting 148
5.3.3 RepurposingandExpandingUtilityofAntibiotics 149
5.3.3.1 VirtualDrugScreensInformedbyMetabolicReconstructions 149
5.3.3.2 LimitingResistancewithDrugCombinations 149
5.3.3.3 ImprovingTreatmentOptionsbyIncreasingSensitivityto
Antibiotics 150
5.3.4 ImprovingToxicityScreenswiththeHumanMetabolicNetwork
Reconstruction 150
5.4 TheHumanReconstruction 151
5.4.1 ApproachesfortheHumanReconstruction 152
5.4.2 TargetIdentification 152
5.4.2.1 DrugTargetinginCancer 152
5.4.2.2 DrugTargetinginMetabolicDiseases 153
5.4.3 ToxicityandOtherSideEffects 154
5.5 CommunityModels 155
5.5.1 Host–PathogenCommunityModels 155
5.5.2 EukaryoticCommunityModels 156
5.6 PersonalizedMedicine 156
5.7 Conclusion 157
References 158
6 ComputationalModelingofMicrobialCommunities 163
SiuH.J.Chan,MargaretSimons,andCostasD.Maranas
Summary 163
6.1 Introduction 163
6.1.1 MicrobialCommunities 163
6.1.2 ModelingMicrobialCommunities 165
6.1.3 ModelStructures 165
6.1.4 QuantitativeApproaches 166
6.2 EcologicalModels 168
6.2.1 GeneralizedPredator–PreyModel 169
6.2.2 EvolutionaryGameTheory 170
6.2.3 ModelsIncludingAdditionalDimensions 171
6.2.4 AdvantagesandDisadvantages 171
6.3 Genome-ScaleMetabolicModels 172
6.3.1 IntroductionandApplications 172
6.3.2 Genome-ScaleMetabolicModelingofMicrobialCommunities 174
6.3.3 SimulationofMicrobialCommunitiesAssumingSteadyState 175
6.3.3.1 PredictingInteractionsUsingFBA 175
6.3.3.2 IdentifyingMinimalMediabyMixedIntegerLinear
Programming 176
Contents IX
6.3.3.3 ParetoOptimalityAnalysisbyFBA 176
6.3.3.4 ModelingChemostatCo-culture 177
6.3.3.5 CommunityFBAwithCommunityMassBalance 177
6.3.4 DynamicSimulationofMultispeciesModels 177
6.3.5 SpatialandTemporalModelingofCommunities 178
6.3.6 UsingBilevelOptimizationtoCaptureMultipleObjective
Functions 179
6.3.6.1 OptCom 179
6.3.6.2 d-OptCom 181
6.3.6.3 CASINOToolbox 181
6.3.6.4 AdvantagesandDisadvantages 182
6.3.6.5 CurrentChallengesandFutureDirections 182
6.4 ConcludingRemarks 183
References 183
7 DrugTargetingoftheHumanMicrobiome 191
HuaLing,JeeL.Foo,GourvenduSaxena,SanjaySwarup,
andMatthewW.Chang
Summary 191
7.1 Introduction 191
7.2 TheHumanMicrobiome 192
7.3 AssociationoftheHumanMicrobiomewithHuman
Diseases 194
7.3.1 Nasal–SinusDiseases 194
7.3.2 GutDiseases 194
7.3.3 CardiovascularDiseases 196
7.3.4 MetabolicDisorders 196
7.3.5 AutoimmuneDisorders 197
7.3.6 LungDiseases 197
7.3.7 SkinDiseases 197
7.4 DrugTargetingoftheHumanMicrobiome 198
7.4.1 Prebiotics 198
7.4.2 Probiotics 200
7.4.3 Antimicrobials 201
7.4.3.1 Antibiotics 201
7.4.3.2 AntimicrobialPeptides 202
7.4.4 SignalingInhibitors 202
7.4.5 Metabolites 203
7.4.5.1 Short-ChainFattyAcids 203
7.4.5.2 BileAcids 203
7.4.6 MetaboliteReceptorsandEnzymes 204
7.4.6.1 MetaboliteReceptors 204
7.4.6.2 MetabolicEnzymes 204
7.4.7 Microbiome-AidedDrugMetabolism 205
7.4.7.1 DrugDeliveryandRelease 205
X Contents
7.4.7.2 DrugToxicity 206
7.4.8 ImmuneModulators 206
7.4.9 SyntheticCommensalMicrobes 207
7.5 FuturePerspectives 207
7.6 ConcludingRemarks 208
Acknowledgments 208
References 209
8 TowardGenome-ScaleModelsofSignalTransduction
Networks 215
UlrikeMünzner,TimoLubitz,EddaKlipp,andMarcusKrantz
8.1 Introduction 215
8.2 ThePotentialofNetworkReconstruction 219
8.3 InformationTransferNetworks 222
8.4 ApproachestoReconstructionofITNs 225
8.5 TherxnconApproachtoITNWR 230
8.6 TowardQuantitativeAnalysisandModelingofLarge
ITNs 234
8.7 ConclusionandOutlook 236
Acknowledgments 236
Glossary 237
References 238
9 SystemsBiologyofAging 243
JohannesBorgqvist,RiccardoDainese,andMarijaCvijovic
Summary 243
9.1 Introduction 243
9.2 TheBiologyofAging 245
9.3 TheMathematicsofAging 249
9.3.1 DatabasesDevotedtoAgingResearch 249
9.3.2 MathematicalModelinginAgingResearch 249
9.3.3 DistributionofDamagedProteinsduringCellDivision:A
MathematicalPerspective 256
9.3.3.1 CellGrowth 256
9.3.3.2 CellDeath 257
9.3.3.3 CellDivision 257
9.4 FutureChallenges 260
ConflictofInterest 262
References 262
10 ModelingtheDynamicsoftheImmuneResponse 265
ElenaAbad,PabloVilloslada,andJordiGarcía-Ojalvo
10.1 Background 265
10.2 DynamicsofNF-κBSignaling 266
10.2.1 FunctionalRoleandRegulationofNF-κB 266